We propose Dirichlet process mixture (DPM) models for prediction and cluster-wise variable selection, based on two choices of shrinkage baseline prior distributions the linear regression coefficients, namely, Horseshoe Normal-Gamma prior. show in a simulation study that each proposed DPM tends to outperform standard model non-shrinkage normal prior, terms predictive, clustering accuracy. This i...

Summary This paper introduces a new data-driven methodology for estimating sparse covariance matrices of the random coefficients in logit mixture models. Researchers typically specify models under one two extreme assumptions: either an unrestricted full matrix (allowing correlations between all coefficients), or restricted diagonal no at all). Our objective is to find optimal subsets correlated...